Neuroblastomas are cancers of neural crest origin with variable prognoses depending on age at presentation, stage, histology, presence of MYCN amplification, chromosomal ploidy, and deletion status of 1p36. Very little is known of the molecular mechanisms that confer good or poor prognosis in this and other malignancies. We have recently demonstrated that cancers can be diagnosed on the basis of gene expression profiling using cDNA microarrays and sophisticated pattern recognition algorithms such as Artificial Neural Networks. The Oncogenomics Section has expanded this concept further by profiling a series on neuroblastomas of different stages and prognosis. With these methods we are identifying tumor-specific expression patterns, or fingerprints, that uniquely identify a poor prognostic group, as well as those associated with specific genetic aberrations including MYCN amplification. By these techniques, we hope to classify expression profiles that correlate with prognosis and hence identified the genes that confer these biological properties. Once we have narrowed down the list of genes that defines a particular cancer or diagnostic or prognostic group cluster to a minimum number, we will use this to make smaller microarrays or other multiplex PCR-based assays for diagnostic purposes in the clinic. Isotope-coded affinity tags (ICAT), allows the quantitative measurement of protein expression levels in different cell types and tissues. In this method proteins from two samples can be compared by chemically labeling both samples with the light and heavy isotopic forms of a reagent respectively. With this method we plan to sequence and identify up to 3000-4000 differentially expressed proteins between tumors with poor (death) and good (event free survival &gt;3yrs) outcome. These proteins represent potential targets for therapy, diagnostic and prognostic markers for high-risk patients as well as provide important clues on the biology of these tumors that fail to respond to conventional therapy.Neuroblastomas are cancers of neural crest origin with variable prognoses depending on age at presentation, stage, histology, presence of MYCN amplification, chromosomal ploidy, and deletion status of 1p36. Very little is known of the molecular mechanisms that confer good or poor prognosis in this and other malignancies. We have recently demonstrated that cancers can be diagnosed on the basis of gene expression profiling using cDNA microarrays and sophisticated pattern recognition algorithms such as Artificial Neural Networks. The Oncogenomics Section has expanded this concept further by profiling a series on neuroblastomas of different stages and prognosis. With these methods we are identifying tumor-specific expression patterns, or fingerprints, that uniquely identify a poor prognostic group, as well as those associated with specific genetic aberrations including MYCN amplification. By these techniques, we hope to classify expression profiles that correlate with prognosis and hence identified the genes that confer these biological properties. Once we have narrowed down the list of genes that defines a particular cancer or diagnostic or prognostic group cluster to a minimum number, we will use this to make smaller microarrays or other multiplex PCR-based assays for diagnostic purposes in the clinic. Isotope-coded affinity tags (ICAT), allows the quantitative measurement of protein expression levels in different cell types and tissues. In this method proteins from two samples can be compared by chemically labeling both samples with the light and heavy isotopic forms of a reagent respectively. With this method we plan to sequence and identify up to 3000-4000 differentially expressed proteins between tumors with poor (death) and good (event free survival &gt;3yrs) outcome. These proteins represent potential targets for therapy, diagnostic and prognostic markers for high-risk patients as well as provide important clues on the biology of these tumors that fail to respond to conventional therapy.